def __init__(self,
probs=None,
seed=None,
dtype=mstype.int32,
name="Bernoulli"):
"""
Constructor of Bernoulli.
"""
param = dict(locals())
param['param_dict'] = {'probs': probs}
valid_dtype = mstype.int_type + mstype.uint_type + mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
super(Bernoulli, self).__init__(seed, dtype, name, param)
self._probs = self._add_parameter(probs, 'probs')
if self._probs is not None:
check_prob(self.probs)
# ops needed for the class
self.exp = exp_generic
self.log = log_generic
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.const = P.ScalarToArray()
self.floor = P.Floor()
self.fill = P.Fill()
self.less = P.Less()
self.shape = P.Shape()
self.select = P.Select()
self.uniform = C.uniform
def __init__(self,
rate=None,
seed=None,
dtype=mstype.float32,
name="Poisson"):
"""
Constructor of Poisson.
"""
param = dict(locals())
param['param_dict'] = {'rate': rate}
valid_dtype = mstype.int_type + mstype.uint_type + mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype, type(self).__name__)
super(Poisson, self).__init__(seed, dtype, name, param)
self._rate = self._add_parameter(rate, 'rate')
if self.rate is not None:
check_greater_zero(self.rate, 'rate')
# ops needed for the class
self.exp = exp_generic
self.log = log_generic
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.floor = P.Floor()
self.dtypeop = P.DType()
self.shape = P.Shape()
self.fill = P.Fill()
self.less = P.Less()
self.equal = P.Equal()
self.select = P.Select()
self.lgamma = nn.LGamma()
self.igamma = nn.IGamma()
self.poisson = C.poisson
def _add_parameter(self, value, name):
"""
Cast `value` to a tensor and add it to `self.default_parameters`.
Add `name` into and `self.parameter_names`.
"""
# initialize the attributes if they do not exist yet
if not hasattr(self, 'default_parameters'):
self.default_parameters = []
self.parameter_names = []
self.common_dtype = None
# cast value to a tensor if it is not None
if isinstance(value, bool) or value is None:
raise TypeError(f"{name} cannot be type {type(value)}")
value_t = Tensor(value)
# if the bijector's dtype is not specified
if self.dtype is None:
if self.common_dtype is None:
self.common_dtype = value_t.dtype
elif value_t.dtype != self.common_dtype:
raise TypeError(
f"{name} should have the same dtype as other arguments.")
# check if the parameters are casted into float-type tensors
validator.check_type_name(
f"dtype of {name}", value_t.dtype, mstype.float_type, type(self).__name__)
# check if the dtype of the input_parameter agrees with the bijector's dtype
elif value_t.dtype != self.dtype:
raise TypeError(
f"{name} should have the same dtype as the bijector's dtype.")
self.default_parameters += [value,]
self.parameter_names += [name,]
return value_t
def __init__(self,
rate=None,
seed=None,
dtype=mstype.float32,
name="Exponential"):
"""
Constructor of Exponential.
"""
param = dict(locals())
param['param_dict'] = {'rate': rate}
valid_dtype = mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
super(Exponential, self).__init__(seed, dtype, name, param)
self._rate = self._add_parameter(rate, 'rate')
if self.rate is not None:
check_greater_zero(self.rate, 'rate')
self.minval = np.finfo(np.float).tiny
# ops needed for the class
self.exp = exp_generic
self.log = log_generic
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.const = P.ScalarToArray()
self.dtypeop = P.DType()
self.fill = P.Fill()
self.less = P.Less()
self.select = P.Select()
self.shape = P.Shape()
self.uniform = C.uniform
def __init__(self,
mean=None,
sd=None,
seed=None,
dtype=mstype.float32,
name="Normal"):
"""
Constructor of Normal.
"""
param = dict(locals())
param['param_dict'] = {'mean': mean, 'sd': sd}
valid_dtype = mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
super(Normal, self).__init__(seed, dtype, name, param)
self._mean_value = self._add_parameter(mean, 'mean')
self._sd_value = self._add_parameter(sd, 'sd')
if self._sd_value is not None:
check_greater_zero(self._sd_value, "Standard deviation")
# ops needed for the class
self.exp = exp_generic
self.expm1 = P.Expm1()
self.log = log_generic
self.erf = P.Erf()
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.const = P.ScalarToArray()
self.shape = P.Shape()
self.sq = P.Square()
self.sqrt = P.Sqrt()
def __init__(self,
loc,
scale,
seed=0,
dtype=mstype.float32,
name="Gumbel"):
"""
Constructor of Gumbel distribution.
"""
valid_dtype = mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype, type(self).__name__)
gumbel_cdf = msb.GumbelCDF(loc, scale)
super(Gumbel, self).__init__(
distribution=msd.Uniform(0.0, 1.0, dtype=dtype),
bijector=msb.Invert(gumbel_cdf),
seed=seed, name=name)
# overwrite default_parameters and parameter_names
self._reset_parameters()
self._loc = self._add_parameter(loc, 'loc')
self._scale = self._add_parameter(scale, 'scale')
self._gumbel_bijector = gumbel_cdf
# ops needed for the class
self.cast = P.Cast()
self.const = P.ScalarToArray()
self.exp = exp_generic
self.expm1 = expm1_generic
self.fill = P.Fill()
self.lgamma = nn.LGamma()
self.log = log_generic
self.shape = P.Shape()
self.sqrt = P.Sqrt()
def __init__(self,
probs=None,
seed=None,
dtype=mstype.int32,
name="Categorical"):
param = dict(locals())
param['param_dict'] = {'probs': probs}
valid_dtype = mstype.uint_type + mstype.int_type + mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
super(Categorical, self).__init__(seed, dtype, name, param)
self._probs = self._add_parameter(probs, 'probs')
if self.probs is not None:
check_rank(self.probs)
check_prob(self.probs)
check_sum_equal_one(probs)
# update is_scalar_batch and broadcast_shape
# drop one dimension
if self.probs.shape[:-1] == ():
self._is_scalar_batch = True
self._broadcast_shape = self._broadcast_shape[:-1]
self.argmax = P.ArgMaxWithValue(axis=-1)
self.broadcast = broadcast_to
self.cast = P.Cast()
self.clip_by_value = C.clip_by_value
self.concat = P.Concat(-1)
self.cumsum = P.CumSum()
self.dtypeop = P.DType()
self.exp = exp_generic
self.expand_dim = P.ExpandDims()
self.fill = P.Fill()
self.gather = P.GatherNd()
self.greater = P.Greater()
self.issubclass = P.IsSubClass()
self.less = P.Less()
self.log = log_generic
self.log_softmax = P.LogSoftmax()
self.logicor = P.LogicalOr()
self.logicand = P.LogicalAnd()
self.multinomial = P.Multinomial(seed=self.seed)
self.reshape = P.Reshape()
self.reduce_sum = P.ReduceSum(keep_dims=True)
self.select = P.Select()
self.shape = P.Shape()
self.softmax = P.Softmax()
self.squeeze = P.Squeeze()
self.squeeze_first_axis = P.Squeeze(0)
self.squeeze_last_axis = P.Squeeze(-1)
self.square = P.Square()
self.transpose = P.Transpose()
self.is_nan = P.IsNan()
self.index_type = mstype.int32
self.nan = np.nan
def __init__(self,
concentration1=None,
concentration0=None,
seed=None,
dtype=mstype.float32,
name="Beta"):
"""
Constructor of Beta.
"""
param = dict(locals())
param['param_dict'] = {
'concentration1': concentration1,
'concentration0': concentration0
}
valid_dtype = mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
# As some operators can't accept scalar input, check the type here
if isinstance(concentration0, float):
raise TypeError("Input concentration0 can't be scalar")
if isinstance(concentration1, float):
raise TypeError("Input concentration1 can't be scalar")
super(Beta, self).__init__(seed, dtype, name, param)
self._concentration1 = self._add_parameter(concentration1,
'concentration1')
self._concentration0 = self._add_parameter(concentration0,
'concentration0')
if self._concentration1 is not None:
check_greater_zero(self._concentration1, "concentration1")
if self._concentration0 is not None:
check_greater_zero(self._concentration0, "concentration0")
# ops needed for the class
self.log = log_generic
self.log1p = P.Log1p()
self.neg = P.Neg()
self.pow = P.Pow()
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.fill = P.Fill()
self.shape = P.Shape()
self.select = P.Select()
self.logicaland = P.LogicalAnd()
self.greater = P.Greater()
self.digamma = nn.DiGamma()
self.lbeta = nn.LBeta()
def __init__(self,
loc=None,
scale=None,
seed=None,
dtype=mstype.float32,
name="Logistic"):
"""
Constructor of Logistic.
"""
param = dict(locals())
param['param_dict'] = {'loc': loc, 'scale': scale}
valid_dtype = mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
super(Logistic, self).__init__(seed, dtype, name, param)
self._loc = self._add_parameter(loc, 'loc')
self._scale = self._add_parameter(scale, 'scale')
if self._scale is not None:
check_greater_zero(self._scale, "scale")
# ops needed for the class
self.cast = P.Cast()
self.const = P.ScalarToArray()
self.consttensor = P.ScalarToTensor()
self.dtypeop = P.DType()
self.exp = exp_generic
self.expm1 = P.Expm1()
self.fill = P.Fill()
self.less = P.Less()
self.log = log_generic
self.log1p = P.Log1p()
self.logicalor = P.LogicalOr()
self.erf = P.Erf()
self.greater = P.Greater()
self.sigmoid = P.Sigmoid()
self.squeeze = P.Squeeze(0)
self.select = P.Select()
self.shape = P.Shape()
self.softplus = self._softplus
self.sqrt = P.Sqrt()
self.uniform = C.uniform
self.threshold = np.log(np.finfo(np.float32).eps) + 1.
self.tiny = np.finfo(np.float).tiny
self.sd_const = np.pi / np.sqrt(3)
def __init__(self,
concentration=None,
rate=None,
seed=None,
dtype=mstype.float32,
name="Gamma"):
"""
Constructor of Gamma.
"""
param = dict(locals())
param['param_dict'] = {'concentration': concentration, 'rate': rate}
valid_dtype = mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
# As some operators can't accept scalar input, check the type here
if isinstance(concentration, (int, float)):
raise TypeError("Input concentration can't be scalar")
if isinstance(rate, (int, float)):
raise TypeError("Input rate can't be scalar")
super(Gamma, self).__init__(seed, dtype, name, param)
self._concentration = self._add_parameter(concentration,
'concentration')
self._rate = self._add_parameter(rate, 'rate')
if self._concentration is not None:
check_greater_zero(self._concentration, "concentration")
if self._rate is not None:
check_greater_zero(self._rate, "rate")
# ops needed for the class
self.log = log_generic
self.square = P.Square()
self.sqrt = P.Sqrt()
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.dtypeop = P.DType()
self.fill = P.Fill()
self.shape = P.Shape()
self.select = P.Select()
self.greater = P.Greater()
self.lgamma = nn.LGamma()
self.digamma = nn.DiGamma()
self.igamma = nn.IGamma()
def __init__(self,
bijector,
distribution,
seed=None,
name="transformed_distribution"):
"""
Constructor of transformed_distribution class.
"""
param = dict(locals())
validator.check_value_type('bijector', bijector,
[nn.probability.bijector.Bijector],
type(self).__name__)
validator.check_value_type('distribution', distribution,
[Distribution],
type(self).__name__)
validator.check_type_name("dtype", distribution.dtype,
mstype.float_type,
type(self).__name__)
super(TransformedDistribution, self).__init__(seed, distribution.dtype,
name, param)
self._bijector = bijector
self._distribution = distribution
# set attributes
self._is_linear_transformation = self.bijector.is_constant_jacobian
self._dtype = self.distribution.dtype
self._is_scalar_batch = self.distribution.is_scalar_batch and self.bijector.is_scalar_batch
self._batch_shape = self.distribution.batch_shape
self.default_parameters = self.distribution.default_parameters
self.parameter_names = self.distribution.parameter_names
# by default, set the parameter_type to be the distribution's parameter_type
self.parameter_type = self.distribution.parameter_type
self.exp = exp_generic
self.log = log_generic
self.isnan = P.IsNan()
self.cast_base = P.Cast()
self.equal_base = P.Equal()
self.select_base = P.Select()
self.fill_base = P.Fill()
# broadcast bijector batch_shape and distribution batch_shape
self._broadcast_shape = self._broadcast_bijector_dist()
def __init__(self,
is_constant_jacobian=False,
is_injective=True,
name=None,
dtype=None,
param=None):
"""
Constructor of Bijector class.
"""
super(Bijector, self).__init__()
validator.check_value_type('name', name, [str], type(self).__name__)
validator.check_value_type('is_constant_jacobian',
is_constant_jacobian, [bool], name)
validator.check_value_type('is_injective', is_injective, [bool], name)
if dtype is not None:
validator.check_type_name("dtype", dtype, mstype.float_type,
type(self).__name__)
self._name = name
self._dtype = dtype
self._parameters = {}
# parsing parameters
for k in param.keys():
if k == 'param':
continue
if not (k == 'self' or k.startswith('_')):
self._parameters[k] = param[k]
# if no bijector is used as an argument during initilization
if 'bijector' not in param.keys():
self._batch_shape = self._calc_batch_shape()
self._is_scalar_batch = self._check_is_scalar_batch()
self._is_constant_jacobian = is_constant_jacobian
self._is_injective = is_injective
self.context_mode = context.get_context('mode')
self.checktensor = CheckTensor()
# ops needed for the base class
self.cast_base = P.Cast()
self.dtype_base = P.DType()
self.shape_base = P.Shape()
self.fill_base = P.Fill()
self.sametypeshape_base = P.SameTypeShape()
self.issubclass_base = P.IsSubClass()
def __init__(self,
loc=None,
scale=None,
seed=None,
dtype=mstype.float32,
name="Cauchy"):
"""
Constructor of Cauchy.
"""
param = dict(locals())
param['param_dict'] = {'loc': loc, 'scale': scale}
valid_dtype = mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
super(Cauchy, self).__init__(seed, dtype, name, param)
self._loc = self._add_parameter(loc, 'loc')
self._scale = self._add_parameter(scale, 'scale')
if self._scale is not None:
check_greater_zero(self._scale, "scale")
# ops needed for the class
self.atan = P.Atan()
self.cast = P.Cast()
self.const = P.ScalarToArray()
self.dtypeop = P.DType()
self.exp = exp_generic
self.fill = P.Fill()
self.less = P.Less()
self.log = log_generic
self.log1p = log1p_generic
self.squeeze = P.Squeeze(0)
self.shape = P.Shape()
self.sq = P.Square()
self.sqrt = P.Sqrt()
self.tan = P.Tan()
self.uniform = C.uniform
self.entropy_const = np.log(4 * np.pi)
def __init__(self,
low=None,
high=None,
seed=None,
dtype=mstype.float32,
name="Uniform"):
"""
Constructor of Uniform distribution.
"""
param = dict(locals())
param['param_dict'] = {'low': low, 'high': high}
valid_dtype = mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
super(Uniform, self).__init__(seed, dtype, name, param)
self._low = self._add_parameter(low, 'low')
self._high = self._add_parameter(high, 'high')
if self.low is not None and self.high is not None:
check_greater(self.low, self.high, 'low', 'high')
# ops needed for the class
self.exp = exp_generic
self.log = log_generic
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.const = P.ScalarToArray()
self.dtypeop = P.DType()
self.fill = P.Fill()
self.less = P.Less()
self.lessequal = P.LessEqual()
self.logicaland = P.LogicalAnd()
self.select = P.Select()
self.shape = P.Shape()
self.sq = P.Square()
self.zeroslike = P.ZerosLike()
self.uniform = C.uniform
def __init__(self,
probs=None,
seed=None,
dtype=mstype.int32,
name="Geometric"):
"""
Constructor of Geometric distribution.
"""
param = dict(locals())
param['param_dict'] = {'probs': probs}
valid_dtype = mstype.int_type + mstype.uint_type + mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
super(Geometric, self).__init__(seed, dtype, name, param)
self._probs = self._add_parameter(probs, 'probs')
if self._probs is not None:
check_prob(self.probs)
self.minval = np.finfo(np.float).tiny
# ops needed for the class
self.exp = exp_generic
self.log = log_generic
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.const = P.ScalarToArray()
self.dtypeop = P.DType()
self.fill = P.Fill()
self.floor = P.Floor()
self.issubclass = P.IsSubClass()
self.less = P.Less()
self.pow = P.Pow()
self.select = P.Select()
self.shape = P.Shape()
self.sq = P.Square()
self.uniform = C.uniform
def _check_label_dtype(labels_dtype, cls_name):
validator.check_type_name("labels", labels_dtype,
[mstype.int32, mstype.int64], cls_name)
def __init__(self, op, to_type=mstype.float16):
super(OutputTo, self).__init__(auto_prefix=False)
self._op = op
validator.check_type_name('to_type', to_type,
[mstype.float16, mstype.float32], None)
self.to_type = to_type
def _check_input_dtype(input_dtype, param_name, allow_dtypes, cls_name):
validator.check_type_name(param_name, input_dtype, allow_dtypes, cls_name)
def _check_input_dtype(targets_dtype, cls_name):
validator.check_type_name(
"targets", targets_dtype,
[mstype.int32, mstype.int64, mstype.float16, mstype.float32], cls_name)
